| 1. |
- Kristoffersson, Anders, 1985-
(författare)
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Study Design and Dose Regimen Evaluation of Antibiotics based on Pharmacokinetic and Pharmacodynamic Modelling
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Current excessive use and abuse of antibiotics has resulted in increasing bacterial resistance to common treatment options which is threatening to deprive us of a pillar of modern medicine. In this work methods to optimize the use of existing antibiotics and to help development of new antibiotics were developed and applied.Semi-mechanistic pharmacokinetic-pharmacodynamic (PKPD) models were developed to describe the time course of the dynamic effect and interaction of combinations of antibiotics. The models were applied to illustrate that colistin combined with a high dose of meropenem may overcome meropenem-resistant P. aeruginosa infections.The results from an in vivo dose finding study of meropenem was successfully predicted by the meropenem PKPD model in combination with a murine PK model, which supports model based dosage selection. However, the traditional PK/PD index based dose selection was predicted to have poor extrapolation properties from pre-clinical to clinical settings, and across patient populations.The precision of the model parameters, and hence the model predictions, is dependent on the experimental design. A limited study design is dictated by cost and, for in vivo studies, ethical reasons. In this work optimal design (OD) was demonstrated to be able to reduce the experimental effort in time-kill curve experiments and was utilized to suggest the experimental design for identification and estimation of an interaction between antibiotics.OD methods to handle inter occasion variability (IOV) in optimization of individual PK parameter estimates were proposed. The strategy was applied in the design of a sparse sampling schedule that aim to estimate individual exposures of colistin in a multi-centre clinical study. Plasma concentration samples from the first 100 patients have been analysed and indicate that the performance of the design is close to the predicted.The methods described in this thesis holds promise to facilitate the development of new antibiotics and to improve the use of existing antibiotics.
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| 2. |
- Bender, Brendan, 1967-
(författare)
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Pharmacometric Models for Antibody Drug Conjugates and Taxanes in HER2+ and HER2- Breast Cancer
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In oncology, there is a need to optimize drug treatment for efficient eradication of tumors, minimization of adverse effects (AEs), and prolonging patient survival. Pharmacometric models can be developed to streamline information between drug development phases, describe and quantify response to treatment, and determine dose regimens that balance toxicity and efficacy. In this thesis, data from trastuzumab emtansine (T-DM1) and taxane drug treatment were used to develop pharmacometric models of pharmacokinetics (PK), AEs, anti-tumor response, and survival, supporting drug development.T-DM1 is an antibody-drug conjugate (ADC) for treatment of human epidermal growth factor receptor 2 (HER2)–positive breast cancer. ADCs are a relatively new class of oncologic agents, and contain multiple drug-to-antibody ratio (DAR) moieties in their dose product. The complex distribution of T-DM1 was elucidated through PK models developed using in vitro and in vivo rat and cynomolgus monkey DAR data. Mechanism–based PK/pharmacodynamic (PKPD) models were also developed for T-DM1 that described the AEs thrombocytopenia (TCP) and hepatotoxicity in patients receiving T-DM1. Variable patterns of platelet and transaminase (ALT and AST) response were quantified, including an effect of Asian ethnicity that was related to higher incidences of TCP. Model simulations, comparing dose intensities (DI) and Grade 3/4 incidences between the approved T-DM1 dose (3.6 mg/kg every three weeks) and weekly regimens, determined that 2.4 mg/kg weekly provided the highest DI.Docetaxel and paclitaxel are taxane treatment options for HER2–negative breast cancer. Tumor response data from these treatments were used to develop a mechanism–based model of tumor quiescence and drug–resistance. Subsequently, a parametric survival analysis found that tumor baseline and the model–predicted time to tumor growth (TTG) were predictors of overall survival (OS). This tumor and OS modeling approach can be applied to other anticancer treatments with similar patterns of drug–resistance.Overall, the pharmacometric models developed within this thesis present new modeling approaches and provide understanding on ADC PK and PKPD (TCP and hepatotoxicity), as well as drug–resistance tumor response. These models can inform simulation strategies and clinical study design, and be applied towards dose finding for anticancer drugs in development, especially ADCs.
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| 3. |
- Centanni, Maddalena, 1994-
(författare)
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Model-based evaluation of biomarkers for dose-individualization in oncology
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In contemporary cancer care, several issues are garnering increasing attention. First, significant inter-individual variability among patients challenges the effectiveness of a uniform dosing approach. Second, the escalating costs of treatments necessitate careful consideration when selecting doses and other clinical modalities, including biomarkers, while balancing economic constraints. The objective of this thesis was to evaluate techniques for tailoring doses and guiding clinical decisions for cancer patients through the development and implementation of various models, with the aim of improving treatment outcomes in terms of both efficacy and safety. Through a model-based framework integrating sunitinib pharmacokinetics (PK), adverse events, biomarkers, tumor dynamics and their correlation with overall survival, different treatment schedules and biomarkers for dose individualization were explored. Based on the proposed threshold values, neutrophil count (ANC) and the biomarker sVEGFR-3 were identified as offering the best balance between safety and efficacy for sunitinib in gastro-intestinal stromal tumors (GIST) and could thus serve as viable guides for dose individualization in clinical practice. Given its routine measurement, dose adjustments guided by ANC may be preferable in clinical settings. The feasibility of utilizing diastolic blood pressure (dBP) for personalized dose optimization of tyrosine-kinase inhibitors in clinical settings is constrained due to its reliance on repeated measurements taken at consistent intervals. For axitinib and sunitinib, model-based predictions using multiple clinical measurements were more accurate than single sample measurements. For drugs with high unexplained inter-individual variability (IIV), low residual variability (RUV), and low inter-occasional variability (IOV), therapeutic drug monitoring (TDM) provided a more accurate measure of exposure. Conversely, for drugs with low IIV and high RUV and IOV, pharmacogenetic profiling was more suitable. However, the prevalence of pharmacogenetic subtypes and the challenge of measuring exposure metrics like AUC through limited sampling also influence these approaches.This research further emphasizes how model structure affects the outcomes of cost-effectiveness analyses and consequently the potential implications for regulatory decisions. Although creating mechanistic models for these analyses demands substantial initial effort, the growing need for model-based analyses in drug approval is likely to make these models more accessible for future compounds. Moreover, such models are expected to be more biologically plausible and therefore more reflective of reality and offer flexibility for exploring alternative dosages with limited additional effort.Using model-based assessments, the relationship between the PK and PK-pharmacodynamic (PKPD) profiles of adverse events arising from therapies for acute lymphocytic leukemia were established. For PEG-asparaginase, the PK model categorized 93% of patients who experienced inactivation against PEG-asparaginase as having an increased clearance, and 86% of patients who did not experience hypersensitivity as maintaining stable clearance throughout their asparaginase treatment. This approach marks a potential method for predicting inactivation by identifying early changes in clearance. For vincristine, model-informed precision dosing was shown to reduce the incidence of vincristine-induced peripheral neuropathy (VIPN) from 62.1% to 53.9%, though the clinical impact remains modest.
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| 4. |
- Netterberg, Ida, 1988-
(författare)
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Pharmacometric Evaluation of Biomarkers to Improve Treatment in Oncology
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cancer is a family of many different diseases with substantial heterogeneity also within the same cancer type. In the era of personalized medicine, it is desirable to identify an early response to treatment (i.e., a biomarker) that can predict the long-term outcome with respect to both safety and efficacy. It is however not uncommon to categorize continuous data, e.g., using tumor size data to classify patients as responders or non-responders, resulting in loss of valuable information. Pharmacometric modeling offers a way of analyzing longitudinal time-courses of different variables (e.g., biomarker and tumor size), and therefore minimizing information loss.Neutropenia is the most common dose-limiting toxicity for chemotherapeutic drugs and manifests by a low absolute neutrophil count (ANC). This thesis explored the potential of using model-based predictions together with frequent monitoring of the ANC to identify patients at risk of severe neutropenia and potential dose delay. Neutropenia may develop into febrile neutropenia (FN), a potentially life-threatening condition. Interleukin 6, an immune-related biomarker, was identified as an on-treatment predictor of FN in breast cancer patients treated with adjuvant chemotherapy. C-reactive protein, another immune-related biomarker, rather demonstrated confirmatory value to support FN diagnosis.Cancer immunotherapy is the most recent advance in anticancer treatment, with immune checkpoint inhibitors, e.g., atezolizumab, leading the breakthrough. In a pharmacometric modeling framework, the area under the curve of atezolizumab was related to tumor size changes in non-small cell lung cancer patients treated with atezolizumab. The relative change from baseline of Interleukin 18 at 21 days after start of treatment added predictive value on top of the drug effect. The tumor size time-course predicted overall survival (OS) in the same population.Circulating tumor cells (CTCs) are tumor cells that have shed from a tumor and circulate in the blood. CTCs may cause distant metastases, which is related to a poor prognosis. A novel modeling framework was developed in which the relationship between tumor size and CTC count was quantified in patients with metastatic colorectal cancer treated with chemotherapy and targeted therapy. It was also demonstrated that the CTC count was a superior predictor of OS in comparison to tumor size changes.In summary, IL-6 predicted FN, IL-18 predicted tumor size changes and tumor size changes and CTC counts predicted OS. The results in this thesis were obtained by using pharmacometrics to evaluate biomarkers to improve treatment in oncology.
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| 5. |
- Grip, Lena, 1978-
(författare)
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Likhetens rum - olikhetens praktik : om produktion av integration i fyra svenska kommuner
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- What is integration? How is integration achieved and to whom is it directed? And how is integration implemented in respect to immigrant women? This thesis examines ideas concerning integration and the policy and practice of integration on the basis of a set of policies which include Swedish society as “multicultural”, with the focus on immigrant women. Integration as a political project exists at a national level in Sweden but also in most of Sweden’s municipalities. This study concentrates mainly on the local level. Four medium-sized Swedish municipalities with differing conditions, political governance, geographical location and immigration history have been selected for a study of how integration is implemented and how ideas about integration also produce and reproduce both local and national space. Various political documents on integration and interviews with politicians, officials and immigrant women form the basis of a study of how integration is achieved as seen from the positions of different actors at the municipal level and how similarities and differences are constructed and expressed in the example of integration. Conceptions regarding similarities and differences on the basis of gender and ethnicity in an imagined Swedish space, but also in more local spaces, have been central to an understanding of the phenomena studied in the thesis, given that at the core of a policy of integration lie differences that need to be integrated. The theoretical points of departure in the thesis are the ideas of Henri Lefebvre regarding the production of space, which are combined with theories inspired by gender theory and phenomenology to illustrate the individual and physical aspects of the process. A model of the complexity of creating spatiality is devised from this theoretical basis and is used throughout the thesis both as an analytical tool and as an instrument for creating structure. On the basis of the study it is concluded that integration may be likened to a space of similarity, as integration is construed by means of different metaphors as a move from something “outside” to Sweden, as a room to be entered. The policy and practice of integration, as it has so far functioned, is shown to be based on a (dis)similarity paradox in that integration is constructed on a discourse of similarity at the same time as assumptions and constructions of difference are a fundamental point of departure for the policy objectives.
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| 6. |
- Hamberg, Anna-Karin, 1964-
(författare)
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Pharmacometric Models for Individualisation of Warfarin in Adults and Children
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Warfarin is one of the most widely used anticoagulants. Therapy is complicated by warfarin’s narrow therapeutic range and pronounced variability in individual dose requirements. Although warfarin therapy is uncommon in children, it is crucial for children with certain congenital or acquired heart diseases. Treatment in children is especially difficult due to the lack of i) a decision support tool for efficient and consistent dose adjustments, and ii) a flexible warfarin formulation for accurate and reproducible dosing.The overall aim of this thesis was to develop a PKPD-based pharmacometric model for warfarin that describes the dose-response relationship over time, and to identify important predictors that influence individual dose requirements both in adults and children. Special emphasis was placed on investigating the contribution of genetic factors to the observed variability.A clinically useful pharmacometric model for warfarin has been developed using NONMEM. The model has been successfully reformulated into a KPD-model that describes the relationship between warfarin dose and INR response, and that is applicable to both adults and children. From a clinical perspective, this is a very important change since it allows the use of information on dose and INR that is available routinely. The model incorporates both patient and clinical characteristics, such as age, weight, CYP2C9 and VKORC1 genotype, and baseline and target INR, for the prediction of an individualised starting dose. It also enables the use of information from previous doses and INR observations to further individualise the dose a posteriori using a Bayesian forecasting method.The NONMEM model has been transferred to a user-friendly, platform independent tool to aid use in clinical practice. The tool can be used for a priori and a posteriori individualisation of warfarin therapy in both adults and children. The tool should ensure consistent dose adjustment practices, and provide more efficient individualisation of warfarin dosing in all patients, irrespective of age, body weight, CYP2C9 or VKORC1 genotype, baseline or target INR. The expected outcome is improved warfarin therapy compared with empirical dosing, with patients achieving a therapeutic and stable INR faster and avoiding high INRs that increase the risk of bleeding.
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| 7. |
- Hansson, Emma K., 1980-
(författare)
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Pharmacometric Models for Biomarkers, Side Effects and Efficacy in Anticancer Drug Therapy
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- New approaches quantifying the effect of treatment are needed in oncology to improve the drug development process and to enable treatment optimization for existing therapies. This thesis focuses on the development of pharmacometric models for biomarkers, side effects and efficacy in order to identify predictors of clinical response in anti-cancer drug therapy. The variability in myelosuppression was characterized in six different cytotoxic anticancer treatments to evaluate a model-based dose individualization approach utilizing neutrophil counts as a biomarker. The estimated impact of inter-occasion variability was relatively low in relation to the inter-individual variability, indicating that myelosuppression is predictable from one treatment course to another. The approach may thereby be useful for dose optimization within an individual. To further study and to identify predictors for the severe side effect febrile neutropenia (FN), the relationship between the shape of the myelosuppression time-course and the probability of FN was characterized. Patients with a rapid decline in neutrophil counts and high drug sensitivity were identified to have a higher probability of developing FN compared with other patients who experience grade 4 neutropenia. Predictors of clinical response in patients receiving sunitinib for the treatment of gastro-intestinal stromal tumor (GIST) were identified by the development of an integrated modeling framework. Drug exposure, biomarkers, tumor dynamics, side effects and overall survival (OS) were linked in a unified structure, and univariate and multivariate exposure variables were tested for their predictive capacities. The soluble biomarker, sVEGFR-3 and tumor size at start of treatment were found to be promising predictors of overall survival, with decreased sVEGFR-3 levels and smaller baseline tumor size being predictive of longer OS. Also hypertension and neutropenia was identified as predictors of OS. The developed modeling framework may be useful to monitor clinical response, optimize dosing in sunitinib and to facilitate dose individualization.
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| 8. |
- Khan, David D.
(författare)
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Pharmacokinetic-Pharmacodynamic modeling and prediction of antibiotic effects
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Problems of emerging antibiotic resistance are becoming a serious threat worldwide, and at the same time, the interest to develop new antimicrobials has declined. There is consequently a need for efficient methods to develop new treatments that minimize the risk of resistance development and that are effective on infections caused by resistant strains. Based on in silico mathematical models, describing the time course of exposure (Pharmacokinetics, PK) and effect (Pharmacodynamics, PD) of a drug, information can be collected and the outcome of various exposures may be predicted. A general model structure, that characterizes the most important features of the system, has advantages as it can be used for different situations. The aim of this thesis was to develop Pharmacokinetic-Pharmacodynamic (PKPD) models describing the bacterial growth and killing after mono- and combination exposures to antibiotics and to explore the predictive ability of PKPD-models across preclinical experimental systems. Models were evaluated on data from other experimental settings, including prediction into animals. A PKPD model characterizing the growth and killing for a range of E. coli bacteria strains, with different MICs, as well as emergence of resistance, was developed. The PKPD model was able to predict results from different experimental conditions including high start inoculum experiments, a range of laboratory and clinical strains as well as experiments where wild-type and mutant bacteria are competing at different drug concentrations. A PKPD model, developed based on in vitro data, was also illustrated to have the capability to replicate the data from an in vivo study. This thesis illustrates the potential of PKPD models to characterize in vitro data and their usage for predictions of different types of experiments. The thesis supports the use of PKPD models to facilitate development of new drugs and to improve the use of existing antibiotics.
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| 9. |
- Madathil Krishnan, Sreenath
(författare)
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Pharmacometrics to improve evaluation and individualization of anticancer drug treatment
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The success rate in clinical development of newer anti-cancer molecules is the lowest compared to other major diseases. Improving the success rate and better early evaluation of efficacy of anti-cancer drugs remain challenging obstacles. Population modelling and model informed drug discovery (MIDD) has been utilized as a fundamental component in facilitating drug development and for decision making in the past decade. The aim of this thesis was to develop pharmacometric approaches to analyze various types of data collected in oncology trials to facilitate drug development and explore the value of model-based dose individualization to improve anticancer drug use.The developed models for the longitudinal tumor size data illustrated that three-dimensional measurements can be more sensitive than current standard - unidimensional measurements - at predicting progression free survival and overall survival. A framework for tumor lesion modeling was developed which allows for quantification of inter-lesion and inter-organ variabilities in tumor dynamics. A new mechanism–based population modelling approach for tumor dynamics model describing sensitive, quiescent and resistant tumor parts was able to characterize the variable tumor response patterns.A new methodology for analyzing survival data in oncology – a parametric multistate model – was developed that can describe the intermediate events and jointly characterize the outcome events including both PFS and OS. In the multistate model frame work, the predictors were evaluated in a prospective manner to not introduce immortal time bias. Furthermore, we applied the multistate model framework to assess the confounding effects of second line treatment in an OS analysis and illustrated that the multistate approach can delineate the impact of second line therapies on survival. Identification of responders and non-responders early after therapy initiation is crucial to trigger treatment modification whenever needed. Highly sensitive methods for tumor response quantification, that correlate with clinical outcome, are therefore required. A simulation study illustrated that the tumor follow-up duration can influence the accuracy in the model derived metrics and thereby impacting the prediction of hazard of death for individual patients. Further a model-based framework was developed that can be used to simulate the potential of biomarker-based dose individualization in sunitinib treatments. Toxicity adjusted dosing and biomarker-based dose adjustment algorithms increased median overall survival as compared to a fixed dose schedule and therapeutic drug monitoring-based dose adjustments, without markedly raising the risk of intolerable toxicities. Model-based dose individualization was suggested to provide a rapid, economical and safe approach to compare various dosing strategies, and guide dose individualization.In summary, the developed modelling approaches provide a better understanding of the relationships between drug exposure, short-term tumor response, and long-term clinical outcome. Such model-based approaches could be applied to improve the efficiency in clinical drug development, and may be used as a support for selecting dose and therapy for individual patients.
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| 10. |
- Schindler, Emilie
(författare)
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Pharmacometrics to improve clinical benefit assessment in oncology
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The high attrition rate in oncology drug development calls for new approaches that would increase the understanding of drugs’ efficacy and safety profiles. This thesis focuses on the development of pharmacometric models to characterize and quantify the relationships between drug exposure, circulating and imaging biomarkers, adverse effects, overall survival (OS), and patient-reported outcomes (PROs).In axitinib-treated metastatic renal cell carcinoma patients, exposure-driven changes in soluble VEGF receptor 3 were linked to tumor size dynamics, which could in turn predict OS better than biomarker- or hypertension-related predictors. In sunitinib-treated gastro-intestinal stromal tumor (GIST) patients, the tumor metabolic response was sensitive to sunitinib dosing schedule and a substantial inter-lesion variability was quantified. A more pronounced decrease in tumor metabolism for the lesion that best responds to treatment after one week was predictive of longer OS. In imatinib-treated GIST patients, tumor volume better detected size changes of liver metastases and were slightly more predictive of OS than conventional tumor diameters, while tumor density had no predictive value.A new modeling approach, the minimal continuous-time Markov model (mCTMM), was developed to facilitate the analysis of ordered categorical scores with Markovian features, e.g. fatigue or hand-foot syndrome grades. The mCTMM is applicable when existing approaches are not appropriate (non-uniform assessment intervals) or not easily implemented (variables with large number of categories).An item response theory pharmacometric framework was established to describe longitudinal item-level data of a PRO questionnaire, the Functional Assessment of Cancer Therapy-Breast (FACT-B). Four correlated latent well-being variables characterized the multi-dimensional nature of FACT-B. When applied to data from breast cancer patients, the progression of physical well-being was typically better in patients treated with ado-trastuzumab emtansine (T-DM1) than with capecitabine-plus-lapatinib-treated patients. No relationship was identified between T-DM1 exposure and any of the latent variables.In summary, the developed models advance the use of pharmacometrics in assessing the clinical benefit of anti-cancer therapies. They provide a quantitative understanding of the desired and adverse responses to drugs, and their relationships to exposure and long-term clinical outcome. Such frameworks may help to early assess response to therapy and optimize dosing strategies for investigational or existing therapies.
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